Pandas Profiling Report

Dataset statistics

Number of variables	14
Number of observations	111857
Missing cells	2694
Missing cells (%)	0.2%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	11.9 MiB
Average record size in memory	112.0 B

Variable types

Categorical	5
Numeric	9

Alerts

`User Comments Added` has constant value "0"	Constant
`Date` has a high cardinality: 1523 distinct values	High cardinality
`Video Title` has a high cardinality: 223 distinct values	High cardinality
`External Video ID` has a high cardinality: 223 distinct values	High cardinality
`Thumbnail link` has a high cardinality: 223 distinct values	High cardinality
`Video Length` is highly correlated with `Average View Percentage`	High correlation
`Views` is highly correlated with `Video Likes Added` and 1 other fields	High correlation
`Video Likes Added` is highly correlated with `Views` and 1 other fields	High correlation
`User Subscriptions Added` is highly correlated with `Views` and 1 other fields	High correlation
`Average View Percentage` is highly correlated with `Video Length`	High correlation
`Views` is highly correlated with `Video Likes Added` and 3 other fields	High correlation
`Video Likes Added` is highly correlated with `Views` and 2 other fields	High correlation
`Video Dislikes Added` is highly correlated with `Views` and 1 other fields	High correlation
`Video Likes Removed` is highly correlated with `Video Dislikes Added`	High correlation
`User Subscriptions Added` is highly correlated with `Views` and 1 other fields	High correlation
`User Subscriptions Removed` is highly correlated with `Views` and 1 other fields	High correlation
`Views` is highly correlated with `Video Likes Added`	High correlation
`Video Likes Added` is highly correlated with `Views` and 1 other fields	High correlation
`User Subscriptions Added` is highly correlated with `Video Likes Added`	High correlation
`Views` is highly correlated with `Video Likes Added` and 4 other fields	High correlation
`Video Likes Added` is highly correlated with `Views` and 4 other fields	High correlation
`Video Dislikes Added` is highly correlated with `Views` and 4 other fields	High correlation
`Video Likes Removed` is highly correlated with `Views` and 2 other fields	High correlation
`User Subscriptions Added` is highly correlated with `Views` and 3 other fields	High correlation
`User Subscriptions Removed` is highly correlated with `Views` and 3 other fields	High correlation
`Average View Percentage` has 1347 (1.2%) missing values	Missing
`Average Watch Time` has 1347 (1.2%) missing values	Missing
`Views` is highly skewed (γ1 = 43.93953763)	Skewed
`Video Likes Added` is highly skewed (γ1 = 36.19381899)	Skewed
`Video Dislikes Added` is highly skewed (γ1 = 139.9320946)	Skewed
`Video Likes Removed` is highly skewed (γ1 = 190.5669346)	Skewed
`User Subscriptions Added` is highly skewed (γ1 = 70.58319419)	Skewed
`User Subscriptions Removed` is highly skewed (γ1 = 31.48082153)	Skewed
`Views` has 1347 (1.2%) zeros	Zeros
`Video Likes Added` has 69081 (61.8%) zeros	Zeros
`Video Dislikes Added` has 109015 (97.5%) zeros	Zeros
`Video Likes Removed` has 106332 (95.1%) zeros	Zeros
`User Subscriptions Added` has 86412 (77.3%) zeros	Zeros
`User Subscriptions Removed` has 110050 (98.4%) zeros	Zeros

Reproduction

Analysis started	2022-04-04 01:11:15.524517
Analysis finished	2022-04-04 01:11:37.139823
Duration	21.62 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

Date
Categorical

HIGH CARDINALITY

Distinct	1523
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Memory size	874.0 KiB

4 Oct 2021	202
20 Sept 2021	201
27 Dec 2021	192
29 Dec 2021	190
30 Dec 2021	190
Other values (1518)	110882

Length

Max length	12
Median length	11
Mean length	10.79518492
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	294 ?
Unique (%)	0.3%

Sample

1st row	19 Jan 2022
2nd row	19 Jan 2022
3rd row	19 Jan 2022
4th row	19 Jan 2022
5th row	19 Jan 2022

Common Values

Value	Count	Frequency (%)
4 Oct 2021	202	0.2%
20 Sept 2021	201	0.2%
27 Dec 2021	192	0.2%
29 Dec 2021	190	0.2%
30 Dec 2021	190	0.2%
6 Jan 2022	189	0.2%
16 Jan 2022	189	0.2%
19 Dec 2021	189	0.2%
18 Jan 2022	189	0.2%
23 Dec 2021	188	0.2%
Other values (1513)	109938	98.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2021	61222	18.2%
2020	39267	11.7%
dec	11614	3.5%
nov	10940	3.3%
oct	10863	3.2%
sept	10123	3.0%
jan	10081	3.0%
aug	10065	3.0%
jul	9614	2.9%
jun	8772	2.6%
Other values (39)	153010	45.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Video Title
Categorical

HIGH CARDINALITY

Distinct	223
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	874.0 KiB

Predicting Crypto-Currency Price Using RNN lSTM & GRU	1522
How to Simulate NBA Games in Python	1105
How I Became A Data Scientist From a Business Background	1046
Should You Get A Masters in Data Science?	1029
Work From Home Data Scientist: Day in the Life	1015
Other values (218)	106140

Length

Max length	100
Median length	53
Mean length	53.70190511
Min length	21

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Kaggle Project From Scratch - Part 2 (Exploratory Data Analysis)
2nd row	Welcome To My Channel \| Ken Jee \| Data Science
3rd row	How She Dominated the FAANG Data Science Interview (@Tina Huang ) - KNN EP. 11
4th row	The 9 Books That Changed My Perspective in 2019
5th row	Interview with the Director of AI Research @ NVIDIA (Anima Anandkumar) - KNN EP. 07

Common Values

Value	Count	Frequency (%)
Predicting Crypto-Currency Price Using RNN lSTM & GRU	1522	1.4%
How to Simulate NBA Games in Python	1105	1.0%
How I Became A Data Scientist From a Business Background	1046	0.9%
Should You Get A Masters in Data Science?	1029	0.9%
Work From Home Data Scientist: Day in the Life	1015	0.9%
Scrape Twitter Data in Python with Twitterscraper Module	1005	0.9%
Predicting Season Long NBA Wins Using Multiple Linear Regression	974	0.9%
Should You Learn R for Data Science?	973	0.9%
My Top 5 Data Science Resources for 2019	951	0.9%
Where YOU Should Start With Data Science Projects	947	0.8%
Other values (213)	101290	90.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
data	100150	9.7%
science	76061	7.4%
	38835	3.8%
to	25369	2.5%
a	25312	2.5%
the	24176	2.4%
how	20324	2.0%
your	15043	1.5%
projects	14899	1.4%
you	14684	1.4%
Other values (610)	673514	65.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

External Video ID
Categorical

HIGH CARDINALITY

Distinct	223
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	874.0 KiB

qfRhKHV8-t4	1522
irjTWNV0eAY	1105
IFceyuL6GZY	1046
RRSRKf9eQxc	1029
4CpmB4TR2C4	1015
Other values (218)	106140

Length

Max length	11
Median length	11
Mean length	11
Min length	11

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	KQ80oD_boBM
2nd row	smeFkHwnM_k
3rd row	vfV4nm004VQ
4th row	3TrAYmrmA8o
5th row	Xgg7dIKys9E

Common Values

Value	Count	Frequency (%)
qfRhKHV8-t4	1522	1.4%
irjTWNV0eAY	1105	1.0%
IFceyuL6GZY	1046	0.9%
RRSRKf9eQxc	1029	0.9%
4CpmB4TR2C4	1015	0.9%
zF_Q2v_9zKY	1005	0.9%
Y_SMU701qlA	974	0.9%
AxP1CL0yaFQ	973	0.9%
tv1e22u2COk	951	0.9%
sq5TnVJWv6A	947	0.8%
Other values (213)	101290	90.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
qfrhkhv8-t4	1522	1.4%
irjtwnv0eay	1105	1.0%
ifceyul6gzy	1046	0.9%
rrsrkf9eqxc	1029	0.9%
4cpmb4tr2c4	1015	0.9%
zf_q2v_9zky	1005	0.9%
y_smu701qla	974	0.9%
axp1cl0yafq	973	0.9%
tv1e22u2cok	951	0.9%
sq5tnvjwv6a	947	0.8%
Other values (213)	101290	90.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Video Length
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	202
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	874.6158756

Minimum	47
Maximum	5029
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	47
5-th percentile	216
Q1	375
median	548
Q3	917
95-th percentile	2686
Maximum	5029
Range	4982
Interquartile range (IQR)	542

Descriptive statistics

Standard deviation	861.2976669
Coefficient of variation (CV)	0.9847725052
Kurtosis	4.773003066
Mean	874.6158756
Median Absolute Deviation (MAD)	211
Skewness	2.194913609
Sum	97831908
Variance	741833.671
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
467	1933	1.7%
375	1757	1.6%
311	1522	1.4%
495	1484	1.3%
484	1397	1.2%
291	1383	1.2%
392	1333	1.2%
59	1277	1.1%
378	1214	1.1%
556	1105	1.0%
Other values (192)	97452	87.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
47	224	0.2%
51	474	0.4%
53	212	0.2%
55	6	< 0.1%
56	436	0.4%
57	41	< 0.1%
59	1277	1.1%
60	167	0.1%
114	169	0.2%
128	427	0.4%

Value	Count	Frequency (%)
5029	149	0.1%
5005	153	0.1%
4518	478	0.4%
4119	651	0.6%
4112	555	0.5%
3735	96	0.1%
3706	428	0.4%
3659	367	0.3%
3493	398	0.4%
3413	168	0.2%

Thumbnail link
Categorical

HIGH CARDINALITY

Distinct	223
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	874.0 KiB

https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	1522
https://i.ytimg.com/vi/irjTWNV0eAY/hqdefault.jpg	1105
https://i.ytimg.com/vi/IFceyuL6GZY/hqdefault.jpg	1046
https://i.ytimg.com/vi/RRSRKf9eQxc/hqdefault.jpg	1029
https://i.ytimg.com/vi/4CpmB4TR2C4/hqdefault.jpg	1015
Other values (218)	106140

Length

Max length	48
Median length	48
Mean length	48
Min length	48

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	https://i.ytimg.com/vi/KQ80oD_boBM/hqdefault.jpg
2nd row	https://i.ytimg.com/vi/smeFkHwnM_k/hqdefault.jpg
3rd row	https://i.ytimg.com/vi/vfV4nm004VQ/hqdefault.jpg
4th row	https://i.ytimg.com/vi/3TrAYmrmA8o/hqdefault.jpg
5th row	https://i.ytimg.com/vi/Xgg7dIKys9E/hqdefault.jpg

Common Values

Value	Count	Frequency (%)
https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	1522	1.4%
https://i.ytimg.com/vi/irjTWNV0eAY/hqdefault.jpg	1105	1.0%
https://i.ytimg.com/vi/IFceyuL6GZY/hqdefault.jpg	1046	0.9%
https://i.ytimg.com/vi/RRSRKf9eQxc/hqdefault.jpg	1029	0.9%
https://i.ytimg.com/vi/4CpmB4TR2C4/hqdefault.jpg	1015	0.9%
https://i.ytimg.com/vi/zF_Q2v_9zKY/hqdefault.jpg	1005	0.9%
https://i.ytimg.com/vi/Y_SMU701qlA/hqdefault.jpg	974	0.9%
https://i.ytimg.com/vi/AxP1CL0yaFQ/hqdefault.jpg	973	0.9%
https://i.ytimg.com/vi/tv1e22u2COk/hqdefault.jpg	951	0.9%
https://i.ytimg.com/vi/sq5TnVJWv6A/hqdefault.jpg	947	0.8%
Other values (213)	101290	90.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
https://i.ytimg.com/vi/qfrhkhv8-t4/hqdefault.jpg	1522	1.4%
https://i.ytimg.com/vi/irjtwnv0eay/hqdefault.jpg	1105	1.0%
https://i.ytimg.com/vi/ifceyul6gzy/hqdefault.jpg	1046	0.9%
https://i.ytimg.com/vi/rrsrkf9eqxc/hqdefault.jpg	1029	0.9%
https://i.ytimg.com/vi/4cpmb4tr2c4/hqdefault.jpg	1015	0.9%
https://i.ytimg.com/vi/zf_q2v_9zky/hqdefault.jpg	1005	0.9%
https://i.ytimg.com/vi/y_smu701qla/hqdefault.jpg	974	0.9%
https://i.ytimg.com/vi/axp1cl0yafq/hqdefault.jpg	973	0.9%
https://i.ytimg.com/vi/tv1e22u2cok/hqdefault.jpg	951	0.9%
https://i.ytimg.com/vi/sq5tnvjwv6a/hqdefault.jpg	947	0.8%
Other values (213)	101290	90.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Views
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	1545
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	49.71428699

Minimum	0
Maximum	35677
Zeros	1347
Zeros (%)	1.2%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	3
median	9
Q3	28
95-th percentile	151
Maximum	35677
Range	35677
Interquartile range (IQR)	25

Descriptive statistics

Standard deviation	316.5575147
Coefficient of variation (CV)	6.367536051
Kurtosis	3395.060358
Mean	49.71428699
Median Absolute Deviation (MAD)	7
Skewness	43.93953763
Sum	5560891
Variance	100208.6601
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	13806	12.3%
2	9821	8.8%
3	7404	6.6%
4	5894	5.3%
5	4841	4.3%
6	4086	3.7%
7	3685	3.3%
8	3313	3.0%
9	3016	2.7%
10	2782	2.5%
Other values (1535)	53209	47.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1347	1.2%
1	13806	12.3%
2	9821	8.8%
3	7404	6.6%
4	5894	5.3%
5	4841	4.3%
6	4086	3.7%
7	3685	3.3%
8	3313	3.0%
9	3016	2.7%

Value	Count	Frequency (%)
35677	1	< 0.1%
31473	1	< 0.1%
26668	1	< 0.1%
20341	1	< 0.1%
16144	1	< 0.1%
15410	1	< 0.1%
15235	1	< 0.1%
14585	1	< 0.1%
14419	1	< 0.1%
13875	1	< 0.1%

Video Likes Added
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	272
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.095532689

Minimum	0
Maximum	1610
Zeros	69081
Zeros (%)	61.8%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	6
Maximum	1610
Range	1610
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	14.26369466
Coefficient of variation (CV)	6.806715418
Kurtosis	2511.935989
Mean	2.095532689
Median Absolute Deviation (MAD)	0
Skewness	36.19381899
Sum	234400
Variance	203.4529854
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	69081	61.8%
1	20179	18.0%
2	7917	7.1%
3	4146	3.7%
4	2486	2.2%
5	1642	1.5%
6	1056	0.9%
7	798	0.7%
8	514	0.5%
9	417	0.4%
Other values (262)	3621	3.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	69081	61.8%
1	20179	18.0%
2	7917	7.1%
3	4146	3.7%
4	2486	2.2%
5	1642	1.5%
6	1056	0.9%
7	798	0.7%
8	514	0.5%
9	417	0.4%

Value	Count	Frequency (%)
1610	1	< 0.1%
1227	1	< 0.1%
774	1	< 0.1%
711	1	< 0.1%
709	1	< 0.1%
657	1	< 0.1%
656	1	< 0.1%
631	1	< 0.1%
626	1	< 0.1%
613	1	< 0.1%

Video Dislikes Added
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	27
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.05242407717

Minimum	0
Maximum	289
Zeros	109015
Zeros (%)	97.5%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	289
Range	289
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.852012656
Coefficient of variation (CV)	35.3275204
Kurtosis	20793.11136
Mean	0.05242407717
Median Absolute Deviation (MAD)	0
Skewness	139.9320946
Sum	5864
Variance	3.429950876
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=27)

Value	Count	Frequency (%)
0	109015	97.5%
1	2176	1.9%
2	370	0.3%
3	119	0.1%
4	69	0.1%
5	34	< 0.1%
6	14	< 0.1%
9	12	< 0.1%
7	10	< 0.1%
8	7	< 0.1%
Other values (17)	31	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	109015	97.5%
1	2176	1.9%
2	370	0.3%
3	119	0.1%
4	69	0.1%
5	34	< 0.1%
6	14	< 0.1%
7	10	< 0.1%
8	7	< 0.1%
9	12	< 0.1%

Value	Count	Frequency (%)
289	2	< 0.1%
284	1	< 0.1%
269	1	< 0.1%
202	1	< 0.1%
64	1	< 0.1%
48	1	< 0.1%
36	1	< 0.1%
35	1	< 0.1%
32	1	< 0.1%
28	1	< 0.1%

Video Likes Removed
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	29
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.08678938287

Minimum	0
Maximum	420
Zeros	106332
Zeros (%)	95.1%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	420
Range	420
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.793531685
Coefficient of variation (CV)	20.66533516
Kurtosis	41055.01462
Mean	0.08678938287
Median Absolute Deviation (MAD)	0
Skewness	190.5669346
Sum	9708
Variance	3.216755906
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=29)

Value	Count	Frequency (%)
0	106332	95.1%
1	4160	3.7%
2	678	0.6%
3	284	0.3%
4	168	0.2%
5	107	0.1%
6	39	< 0.1%
7	35	< 0.1%
8	13	< 0.1%
9	10	< 0.1%
Other values (19)	31	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	106332	95.1%
1	4160	3.7%
2	678	0.6%
3	284	0.3%
4	168	0.2%
5	107	0.1%
6	39	< 0.1%
7	35	< 0.1%
8	13	< 0.1%
9	10	< 0.1%

Value	Count	Frequency (%)
420	1	< 0.1%
355	1	< 0.1%
151	1	< 0.1%
74	1	< 0.1%
53	1	< 0.1%
38	1	< 0.1%
31	1	< 0.1%
25	1	< 0.1%
23	1	< 0.1%
20	1	< 0.1%

User Subscriptions Added
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	198
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.169180293

Minimum	0
Maximum	1844
Zeros	86412
Zeros (%)	77.3%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	3
Maximum	1844
Range	1844
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	12.10906503
Coefficient of variation (CV)	10.35688431
Kurtosis	8300.19818
Mean	1.169180293
Median Absolute Deviation (MAD)	0
Skewness	70.58319419
Sum	130781
Variance	146.629456
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	86412	77.3%
1	13416	12.0%
2	4322	3.9%
3	2134	1.9%
4	1258	1.1%
5	839	0.8%
6	604	0.5%
7	435	0.4%
8	304	0.3%
9	251	0.2%
Other values (188)	1882	1.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	86412	77.3%
1	13416	12.0%
2	4322	3.9%
3	2134	1.9%
4	1258	1.1%
5	839	0.8%
6	604	0.5%
7	435	0.4%
8	304	0.3%
9	251	0.2%

Value	Count	Frequency (%)
1844	1	< 0.1%
1590	1	< 0.1%
803	1	< 0.1%
741	1	< 0.1%
718	1	< 0.1%
685	1	< 0.1%
630	1	< 0.1%
587	1	< 0.1%
571	1	< 0.1%
533	1	< 0.1%

User Subscriptions Removed
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	18
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.0263908383

Minimum	0
Maximum	32
Zeros	110050
Zeros (%)	98.4%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	32
Range	32
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.3139080244
Coefficient of variation (CV)	11.8945833
Kurtosis	1766.305574
Mean	0.0263908383
Median Absolute Deviation (MAD)	0
Skewness	31.48082153
Sum	2952
Variance	0.09853824779
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=18)

Value	Count	Frequency (%)
0	110050	98.4%
1	1384	1.2%
2	215	0.2%
3	72	0.1%
4	41	< 0.1%
5	27	< 0.1%
6	18	< 0.1%
7	11	< 0.1%
9	10	< 0.1%
8	9	< 0.1%
Other values (8)	20	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	110050	98.4%
1	1384	1.2%
2	215	0.2%
3	72	0.1%
4	41	< 0.1%
5	27	< 0.1%
6	18	< 0.1%
7	11	< 0.1%
8	9	< 0.1%
9	10	< 0.1%

Value	Count	Frequency (%)
32	1	< 0.1%
21	1	< 0.1%
16	3	< 0.1%
15	1	< 0.1%
13	3	< 0.1%
12	4	< 0.1%
11	3	< 0.1%
10	4	< 0.1%
9	10	< 0.1%
8	9	< 0.1%

Average View Percentage
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING

Distinct	109659
Distinct (%)	99.2%
Missing	1347
Missing (%)	1.2%
Infinite	0
Infinite (%)	0.0%
Mean	0.3504564736

Minimum	0
Maximum	8.476339587
Zeros	80
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0.02200806075
Q1	0.1780102378
median	0.3368243939
Q3	0.4762565232
95-th percentile	0.7813672404
Maximum	8.476339587
Range	8.476339587
Interquartile range (IQR)	0.2982462854

Descriptive statistics

Standard deviation	0.232565626
Coefficient of variation (CV)	0.663607733
Kurtosis	20.62733331
Mean	0.3504564736
Median Absolute Deviation (MAD)	0.1491538044
Skewness	1.537137424
Sum	38728.9449
Variance	0.05408677038
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	86	0.1%
0	80	0.1%
0.9831568627	27	< 0.1%
0.996672043	22	< 0.1%
0.9974185393	21	< 0.1%
0.9968543307	16	< 0.1%
0.997875	15	< 0.1%
0.9989321429	15	< 0.1%
0.9983235294	14	< 0.1%
0.9995510511	13	< 0.1%
Other values (109649)	110201	98.5%
(Missing)	1347	1.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	80	0.1%
3.311258278 × 10^-6	1	< 0.1%
4.426737494 × 10^-6	1	< 0.1%
8.426966292 × 10^-6	1	< 0.1%
1.805054152 × 10^-5	1	< 0.1%
1.812688822 × 10^-5	1	< 0.1%
1.988466892 × 10^-5	1	< 0.1%
2.102102102 × 10^-5	1	< 0.1%
2.213368747 × 10^-5	1	< 0.1%
3.979591837 × 10^-5	1	< 0.1%

Value	Count	Frequency (%)
8.476339587	1	< 0.1%
6.081182796	1	< 0.1%
4.126801354	1	< 0.1%
3.934207156	1	< 0.1%
3.901702736	1	< 0.1%
3.885998526	1	< 0.1%
2.943820225	1	< 0.1%
2.837104545	1	< 0.1%
2.671567044	1	< 0.1%
2.59559322	1	< 0.1%

Average Watch Time
Real number (ℝ_≥0)

MISSING

Distinct	106978
Distinct (%)	96.8%
Missing	1347
Missing (%)	1.2%
Infinite	0
Infinite (%)	0.0%
Mean	216.7694276

Minimum	0
Maximum	5322.3
Zeros	80
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	874.0 KiB

Quantile statistics

Minimum	0
5-th percentile	17.835
Q1	117.3790804
median	181.505
Q3	268.1268875
95-th percentile	520.7164244
Maximum	5322.3
Range	5322.3
Interquartile range (IQR)	150.7478071

Descriptive statistics

Standard deviation	190.7096512
Coefficient of variation (CV)	0.8797811266
Kurtosis	68.09049549
Mean	216.7694276
Median Absolute Deviation (MAD)	73.09761538
Skewness	5.186095552
Sum	23955189.44
Variance	36370.17106
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	80	0.1%
1	39	< 0.1%
2.9	28	< 0.1%
50.141	27	< 0.1%
0.9	25	< 0.1%
3	24	< 0.1%
4	23	< 0.1%
1.9	22	< 0.1%
2	22	< 0.1%
185.381	22	< 0.1%
Other values (106968)	110198	98.5%
(Missing)	1347	1.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	80	0.1%
0.001	1	< 0.1%
0.003	1	< 0.1%
0.014	1	< 0.1%
0.017	1	< 0.1%
0.018	1	< 0.1%
0.02	3	< 0.1%
0.024	1	< 0.1%
0.035	1	< 0.1%
0.036	1	< 0.1%

Value	Count	Frequency (%)
5322.3	1	< 0.1%
5028.021	1	< 0.1%
5028	1	< 0.1%
5027.66	1	< 0.1%
4701.785	1	< 0.1%
4517.889	1	< 0.1%
4517.15	1	< 0.1%
4517.097	1	< 0.1%
4517.081	1	< 0.1%
4224.958	1	< 0.1%

User Comments Added
Categorical

CONSTANT
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	874.0 KiB

0	111857

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	111857	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	111857	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	Date	Video Title	External Video ID	Video Length	Thumbnail link	Views	User Subscriptions Added	Average View Percentage	Average Watch Time
0	19 Jan 2022	Kaggle Project From Scratch - Part 2 (Exploratory Data Analysis)	KQ80oD_boBM	2191	https://i.ytimg.com/vi/KQ80oD_boBM/hqdefault.jpg	13	0	0.069055	151.300154
1	19 Jan 2022	Welcome To My Channel \| Ken Jee \| Data Science	smeFkHwnM_k	51	https://i.ytimg.com/vi/smeFkHwnM_k/hqdefault.jpg	2	1	0.471255	24.034000
2	19 Jan 2022	How She Dominated the FAANG Data Science Interview (@Tina Huang ) - KNN EP. 11	vfV4nm004VQ	2686	https://i.ytimg.com/vi/vfV4nm004VQ/hqdefault.jpg	10	0	0.126049	338.567500
3	19 Jan 2022	The 9 Books That Changed My Perspective in 2019	3TrAYmrmA8o	980	https://i.ytimg.com/vi/3TrAYmrmA8o/hqdefault.jpg	1	0	0.011508	11.278000
4	19 Jan 2022	Interview with the Director of AI Research @ NVIDIA (Anima Anandkumar) - KNN EP. 07	Xgg7dIKys9E	2904	https://i.ytimg.com/vi/Xgg7dIKys9E/hqdefault.jpg	1	0	0.007541	21.900000
5	19 Jan 2022	Data Science, Machine Learning, and AI: What's the Difference?	q8cEt8gj3zY	266	https://i.ytimg.com/vi/q8cEt8gj3zY/hqdefault.jpg	7	0	0.584489	155.474143
6	19 Jan 2022	The PODCAST you might have asked for?	tnpV1etgcxs	139	https://i.ytimg.com/vi/tnpV1etgcxs/hqdefault.jpg	1	0	0.045813	6.368000
7	19 Jan 2022	#66DaysOfData Round 3 Live Event! (feat. @StatQuest with Josh Starmer)	qUK5Vk4NvBw	3735	https://i.ytimg.com/vi/qUK5Vk4NvBw/hqdefault.jpg	2	0	0.017095	63.850000
8	19 Jan 2022	5 Proven Strategies to Break into a Data Science Job	UpaEjBOMNqs	334	https://i.ytimg.com/vi/UpaEjBOMNqs/hqdefault.jpg	1	0	0.254341	84.950000
9	19 Jan 2022	Reviewing Your Data Science Projects - Episode 7 (Incredible Portfolio Website)	txR8_jGi0Ls	904	https://i.ytimg.com/vi/txR8_jGi0Ls/hqdefault.jpg	4	0	0.362091	327.330250

Last rows

	Date	Video Title	External Video ID	Video Length	Thumbnail link	Views	Average View Percentage	Average Watch Time
111847	27 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	1	0.264855	82.370000
111848	26 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	3	0.509827	158.556333
111849	25 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	1	0.233846	72.726000
111850	24 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	1	0.872392	271.314000
111851	22 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	2	0.502150	156.168500
111852	21 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	2	0.693108	215.556500
111853	20 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	9	0.492501	153.167667
111854	19 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	4	0.087268	27.140250
111855	18 Nov 2017	Predicting Crypto-Currency Price Using RNN lSTM & GRU	qfRhKHV8-t4	311	https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg	13	0.444176	138.138769
111856	1 Nov 2017	ProjectDemoCSC478_UFCFightData	5p73cIRYCZg	729	https://i.ytimg.com/vi/5p73cIRYCZg/hqdefault.jpg	1	0.003018	2.200000

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Phik (φk)

Missing values

Sample

First rows

Last rows